# 导入必要的库和模块
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score 
from tqdm import tqdm

# 加载数字数据集
digits = load_digits()
X = digits.data
y = digits.target

# 将数据集划分为训练集和测试集
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0.0
best_k = 0
knn_model = None

# 初始化一个列表以存储每个k值的准确率
accuracy_for_each_k = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1,41)):
	knn = KNeighborsClassifier(n_neighbors=k)
	knn.fit(X_train,y_train)
	y_pred =knn.predict(X_test)
	accuracy = accuracy_score(y_test,y_pred)
	accuracy_for_each_k.append(accuracy)
	if accuracy > best_accuracy:
		best_accuracy = accuracy
		best_k = k
		knn_model = knn

# 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl','wb') as f:
	pickle.dump(knn_model,f)

# 打印最佳准确率和相应的k值
print("Best Accuracy",best_accuracy)
print("Best K",best_k)

plt.plot(accuracy_for_each_k,linestyle='-')
plt.xlabel('k value')
plt.ylabel('Accuracy')
plt.title('Accuracy of different k values')

plt.axvline(x=best_k, color='red')
plt.text(best_k+0.5,best_accuracy,f'k = {best_k}, accuracy = {best_accuracy:.2f}',
	color='red')
plt.legend()

plt.savefig('accuracy_plot.pdf')

plt.show()